mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 03:34:59 +08:00
Stefan van der Walt
180 lines
4.6 KiB
Python
180 lines
4.6 KiB
Python
"""
|
|
===============================================================
|
|
Comparing edge-based segmentation and region-based segmentation
|
|
===============================================================
|
|
|
|
In this example, we will see how to segment objects from a background. We use
|
|
the ``coins`` image from ``skimage.data``, which shows several coins outlined
|
|
against a darker background.
|
|
"""
|
|
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
from skimage import data
|
|
|
|
coins = data.coins()
|
|
hist = np.histogram(coins, bins=np.arange(0, 256))
|
|
|
|
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
|
|
ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
|
|
ax1.axis('off')
|
|
ax2.plot(hist[1][:-1], hist[0], lw=2)
|
|
ax2.set_title('histogram of grey values')
|
|
|
|
"""
|
|
.. image:: PLOT2RST.current_figure
|
|
|
|
Thresholding
|
|
============
|
|
|
|
A simple way to segment the coins is to choose a threshold based on the
|
|
histogram of grey values. Unfortunately, thresholding this image gives a binary
|
|
image that either misses significant parts of the coins or merges parts of the
|
|
background with the coins:
|
|
|
|
"""
|
|
|
|
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
|
|
ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
|
|
ax1.set_title('coins > 100')
|
|
ax1.axis('off')
|
|
ax2.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
|
|
ax2.set_title('coins > 150')
|
|
ax2.axis('off')
|
|
margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
|
|
fig.subplots_adjust(**margins)
|
|
|
|
"""
|
|
.. image:: PLOT2RST.current_figure
|
|
|
|
|
|
Edge-based segmentation
|
|
=======================
|
|
|
|
Next, we try to delineate the contours of the coins using edge-based
|
|
segmentation. To do this, we first get the edges of features using the Canny
|
|
edge-detector.
|
|
"""
|
|
|
|
from skimage.feature import canny
|
|
edges = canny(coins/255.)
|
|
|
|
fig, ax = plt.subplots(figsize=(4, 3))
|
|
ax.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
|
|
ax.axis('off')
|
|
ax.set_title('Canny detector')
|
|
|
|
"""
|
|
.. image:: PLOT2RST.current_figure
|
|
|
|
These contours are then filled using mathematical morphology.
|
|
"""
|
|
|
|
from scipy import ndimage
|
|
|
|
fill_coins = ndimage.binary_fill_holes(edges)
|
|
|
|
fig, ax = plt.subplots(figsize=(4, 3))
|
|
ax.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
|
|
ax.axis('off')
|
|
ax.set_title('Filling the holes')
|
|
|
|
"""
|
|
.. image:: PLOT2RST.current_figure
|
|
|
|
Small spurious objects are easily removed by setting a minimum size for valid
|
|
objects.
|
|
"""
|
|
from skimage import morphology
|
|
coins_cleaned = morphology.remove_small_objects(fill_coins, 21)
|
|
|
|
fig, ax = plt.subplots(figsize=(4, 3))
|
|
ax.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
|
|
ax.axis('off')
|
|
ax.set_title('Removing small objects')
|
|
|
|
"""
|
|
.. image:: PLOT2RST.current_figure
|
|
|
|
However, this method is not very robust, since contours that are not perfectly
|
|
closed are not filled correctly, as is the case for one unfilled coin above.
|
|
|
|
|
|
Region-based segmentation
|
|
=========================
|
|
|
|
We therefore try a region-based method using the watershed transform. First, we
|
|
find an elevation map using the Sobel gradient of the image.
|
|
|
|
"""
|
|
|
|
from skimage.filters import sobel
|
|
|
|
elevation_map = sobel(coins)
|
|
|
|
fig, ax = plt.subplots(figsize=(4, 3))
|
|
ax.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
|
|
ax.axis('off')
|
|
ax.set_title('elevation_map')
|
|
|
|
"""
|
|
.. image:: PLOT2RST.current_figure
|
|
|
|
Next we find markers of the background and the coins based on the extreme parts
|
|
of the histogram of grey values.
|
|
"""
|
|
|
|
markers = np.zeros_like(coins)
|
|
markers[coins < 30] = 1
|
|
markers[coins > 150] = 2
|
|
|
|
fig, ax = plt.subplots(figsize=(4, 3))
|
|
ax.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
|
|
ax.axis('off')
|
|
ax.set_title('markers')
|
|
|
|
"""
|
|
.. image:: PLOT2RST.current_figure
|
|
|
|
Finally, we use the watershed transform to fill regions of the elevation map
|
|
starting from the markers determined above:
|
|
|
|
"""
|
|
segmentation = morphology.watershed(elevation_map, markers)
|
|
|
|
fig, ax = plt.subplots(figsize=(4, 3))
|
|
ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
|
|
ax.axis('off')
|
|
ax.set_title('segmentation')
|
|
|
|
"""
|
|
.. image:: PLOT2RST.current_figure
|
|
|
|
This last method works even better, and the coins can be segmented and labeled
|
|
individually.
|
|
|
|
"""
|
|
|
|
from skimage.color import label2rgb
|
|
|
|
segmentation = ndimage.binary_fill_holes(segmentation - 1)
|
|
labeled_coins, _ = ndimage.label(segmentation)
|
|
image_label_overlay = label2rgb(labeled_coins, image=coins)
|
|
|
|
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
|
|
ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
|
|
ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
|
|
ax1.axis('off')
|
|
ax2.imshow(image_label_overlay, interpolation='nearest')
|
|
ax2.axis('off')
|
|
|
|
fig.subplots_adjust(**margins)
|
|
|
|
"""
|
|
.. image:: PLOT2RST.current_figure
|
|
|
|
"""
|
|
|
|
plt.show()
|